Template Neural Particle Optimization For Vehicle License Plate Recognition

The need for vehicle recognition has emerged from cases such as security, smart toll collections and traffic monitoring systems. This type of applications produces high demands especially on the accuracy of license plate recognition (LPR). The challenge of LPR is to select the best method for recognizing characters. Since the importance of LPR arises over times, there is a need to find the best alternative to overcome the problem. The detection and extraction of license plate is conventionally based on image processing methods. The image processing method in license plate recognition generally comprises of five stages including pre-processing, morphological operation, feature extraction, segmentation and character recognition. Pre-processing is an initial step in image processing to improve image quality for more suitability in visualizing perception or computational processing while filtering is required to solve contrast enhancement, noise suppression, blurry issue and data reduction. Feature extraction is applied to locate accurately the license plate position and segmentation is used to find and segment the isolated characters on the plates, without losing features of the characters. Finally, character recognition determines each character, identity and displays it into machine readable form. This study introduces five methods of character recognition namely template matching (TM), back-propagation neural network (BPNN), Particle Swarm Optimization neural network (PSONN), hybrid of TM with BPNN (TM-BPNN) and hybrid of TM with PSONN (TM-PSONN). PSONN is proposed as an alternative to train feed-forward neural network, while TM-BPNN and TM-PSONN are proposed to produce a better recognition result. The performance evaluation is carried out based on mean squared error, processing time, number of training iteration, correlation value and percentage of accuracy. The performance of the selected methods was analyzed by making use real images of 300 vehicles. The hybrid of TM-BPNN gives the highest recognition result with 94% accuracy, followed by the hybrid of TM-PSONN with 91.3%, TM with 77.3%, BPNN with 61.7% and lastly PSONN with 37.7%

Automated License Plate Recognition using Existing University Infrastructure and Different Camera Angles

Number or license plate recognition has become an essential technology for traffic and security applications. Providing access control at any organization or academic institution improves the level of security. However, providing security personnel to manually control the access of vehicles at an academic institution is costly, time-consuming, and to a limited extent, error prone. This study investigated the use of an automated vehicle tracking system, incorporating experimental computer vision techniques for license plate recognition that runs in real-time to provide access control for vehicles and provide increased security for an academic institution. A vehicle monitoring framework was designed by using various technologies and experimenting with different camera angles. In addition, the effect of environmental changes on the accuracy of the optical character recognition application was assessed. The Design Science Research methodology was followed to develop the vehicle monitoring framework artifact. Image enhancement algorithms were tested, and the most viable options were evaluated and implemented. Optimal operating criteria that were established for the vehicle monitoring framework achieved a 96% success rate. The results indicate that a cost-effective solution could be provided by using an existing camera infrastructure at an academic institution and suitable license plate recognition software technologies, algorithms, and different camera angles

Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

A Fast Vertical Edge Detection Algorithm for Car License Plate Detection

Recently, License Plate Detection (LPD) has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them worked under restricted conditions, such as fixed illumination, stationary background, and high resolution images. LPD plays an important role in Car License Plate Recognition (CLPR) system because it affects the system's accuracy. This thesis aims to propose a fast vertical edge detector using Vertical Edge Detection Algorithm (VEDA) and to build a Car License Plate Detection (CLPD) method. Pre-processing step is performed in order to enhance and initialize the input image for the next steps. This step is divided into three processes: First, the color image conversion to a gray scale image. Second, an adaptive thresholding is used in order to constitute a binarized image. Third, Unwanted Lines Elimination Algorithm (ULEA) is used in order to enhance the image. The next step is to extract the vertical edges from the 352x288 resolution image by using VEDA. This algorithm is based on the contrast between the values in the binarized image. VEDA is proposed in order to enhance the CLPD method computation time. After the vertical edges have been extracted by VEDA, a morphological operation is used to highlight the vertical details in the image. Next, candidate regions are extracted. Finally, the license plate area is detected. This thesis shows that VEDA is faster than Sobel operator; the results reveal that VEDA is faster than Sobel by about 5-9 times, this range depends on the image resolution. The proposed CLPD method can efficiently detect the license plate area. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 from 643 images are successfully detected. The average accuracy of car license plate detection is 90%. For more evaluation and comparison purposes, the proposed CLPD method is compared with a similar Malaysian license plate detection method, which is CAR Plate Extraction Technology (CARPET). This comparison reveled that the CLPD method is more efficient than CARPET and also has more detection rate